Systems and methods for receiving sensor data for an operating additive manufacturing machine and mapping the sensor data with process data which controls the operation of the machine

ABSTRACT

Method, and corresponding system, for receiving and processing sensor data for an additive manufacturing machine. The method includes determining values of the sensor data relative to time; and determining tool positions relative to time based on process data which controls operation of the additive manufacturing machine. The method further includes determining the sensor data values at the working tool positions based on a correlation of the values of the sensor data relative to time and the working tool positions relative to time. A representation of the sensor data values at the working tool positions is displayed by a user interface device.

FIELD OF THE INVENTION

Exemplary embodiments described herein relate to receiving sensor datafor an operating additive manufacturing machine and mapping the sensordata with process data which controls the operation of the manufacturingmachine.

BACKGROUND

The term “additive manufacturing” refers to processes used to synthesizethree-dimensional objects in which successive layers of material areformed by a manufacturing machine under computer control to create anobject using digital model data from a 3D model. One example of additivemanufacturing is direct metal laser sintering (DMLS), which uses a laserfired into a bed of powdered metal, with the laser being aimedautomatically at points in space defined by a 3D model, thereby meltingthe material together to create a solid structure. The term “directmetal laser melting” (DMLM) may more accurately reflect the nature ofthis process since it typically achieves a fully developed, homogenousmelt pool and fully dense bulk upon solidification. The nature of therapid, localized heating and cooling of the melted material enablesnear-forged material properties, after any necessary heat treatment isapplied.

The DMLS process uses a 3D computer-aided design (CAD) model of theobject to be manufactured, whereby a CAD model data file is created andsent to the fabrication facility. A technician may work with the 3Dmodel to properly orient the geometry for part building and may addsupporting structures to the design, as necessary. Once this “buildfile” has been completed, it is “sliced” into layers of the properthickness for the particular DMLS fabrication machine and downloaded tothe machine to allow the build to begin. The DMLS machine uses, e.g., a200 W Yb-fiber optic laser. Inside the build chamber area, there is amaterial dispensing platform and a build platform along with a recoaterblade used to move new powder over the build platform. The metal powderis fused into a solid part by melting it locally using the focused laserbeam. In this manner, parts are built up additively layer bylayer—typically using layers 20 micrometers thick. This process allowsfor highly complex geometries to be created directly from the 3D CADdata, automatically and without any tooling. DMLS produces parts withhigh accuracy and detail resolution, good surface quality, and excellentmechanical properties.

Defects, such as subsurface porosity, can occur in DMLM processes due tovarious machine, programming, environment, and process parameters. Forexample, deficiencies in machine calibration of mirror positions andlaser focus can result in bulk-fill laser passes not intersectingedge-outline passes. Such deficiencies can result in unfused powder nearthe surface of the component, which may break through the surface tocause defects which cannot be healed by heat treatment finishingprocesses. Another subsurface effect of deficiencies in calibration andmachine programming is excessive dwelling at the turnaround points inraster scanning. Such dwell can result in excessive laser time (i.e.,laser focus time) on a given volume, which may result in “key-holing”and subsurface porosity. Laser and optics degradation, filtration, andother typical laser welding effects can also significantly impactprocess quality, particularly when operating for dozens or hundreds ofhours per build. Furthermore, the DMLM process has thermal shrink anddistortion effects which may require CAD model corrections to bring partdimensions within tolerance.

To reduce such defects, process models could be used to predict localgeometric thermal cycles during the DMLM process, thereby predictingmaterial structure (e.g., grain) and material properties. Models couldalso be used to predict shrink and distortion, which would enablevirtual iteration prior to the actual DMLM process. Furthermore,predictive models could be used to generate a compensated DMLM model todrive the manufacturing machine while accounting for such shrinkage anddistortion.

Conventional models relating build parameters to expected melt poolcharacteristics can be incomplete and/or inaccurate because ofshortcomings in the collection and use of data from sensors monitoringthe manufacturing process. Many of these shortcomings arise becauseconventional manufacturing sensor configurations produce “context-less”data, i.e., data which does not have a frame of reference other thantime of measurement.

Consequently, captured manufacturing sensor data is not in a usefulcontext for design teams to analyze. Another shortcoming of conventionalmanufacturing sensor configurations is that the sensors monitoring aDMLM process may produce unmanageable quantities of data. For example, apyrometer monitoring a build may have a data acquisition rate of 50 kHz,which means that a tremendous quantity of data is produced over a longbuild, e.g., 30 hours.

SUMMARY

Disclosed embodiments provide context-aware data which can lead tomeaningful analytics of manufacturing processes which, in turn, canprovide insights into design changes to improve such processes.Disclosed embodiments contextualize sensor data by linking it tomanufacturing data during the build process. This contextual linkageinforms the development and refinement of “digital twin” models andanalytics to provide insights back to design and engineering to completethe physical-digital feedback loop. Manufacturing and sensor data whichare “contextualized” in this manner result in higher fidelity digitaltwin models for parts.

Disclosed embodiments allow for consistent build quality and anomalydetection in DMLM printing through control and diagnostics of melt poolcharacteristics. Data may be tagged by geometry so that melt poolcharacteristics can be viewed and queried according to layer andscanning pattern. A “part genealogy” may be provided so thatenvironmental conditions are captured when depositing an individuallayer. The knowledge of experienced operators, who may engage in“rounding” by sense of look, listen, and smell, may be effectivelycaptured and digitized. Support may be provided for “debugging” tools toanalyze production failures in real-time. A benchmark standard may beprovided to allow part-to-part, build-to-build, and machine-to-machinecomparison so that build and design tools are no longer in separate“silos.” In-process analysis tools may be provided to avoid relyingsolely on post-build tools.

Disclosed embodiments enable design of experiment (DoE) based oncontextualized data to improve product quality (e.g., part life, finish,etc.) and reproducibility. Insights may be developed regarding how partsare built to inform future design and modeling processes forcomputer-aided design and manufacturing (CAD/CAM). Such informed processchanges help to optimize production effectiveness and reduce cost. Also,key machine performance characteristics may be captured to achieveindividualized CAM.

In one aspect, the disclosed embodiments provide a method, andcorresponding system, for receiving and processing sensor data for anoperating additive manufacturing machine. The method includes receiving,at a first server, the sensor data from a sensor for the additivemachine. The method further includes determining, using a processor ofthe first server, values of the sensor data relative to time; anddetermining, using the processor of the first server, tool positionsrelative to time based on process data which controls operation of themanufacturing machine. The process data includes working vectorsdefining the working tool positions during an additive manufacturingprocess. The method further includes determining the sensor data valuesat the working tool positions based on a correlation of the values ofthe sensor data relative to time and the working tool positions relativeto time; and displaying, on a display of the user interface device, arepresentation of the sensor data values at the working tool positions.

In disclosed embodiments, the method may include one or more of thefollowing features.

The method may further include correlating, using a processor of a userinterface device, a structural model of the part with the sensor datavalues at the working tool positions, such that the displaying includesdisplaying at least a portion of the structural model in correspondencewith the representation of the sensor data values at the working toolpositions.

The method may include segmenting, using the processor of the firstserver, the process data into working vectors and non-working vectors;and filtering, based on the working vectors, the tool positions relativeto time to obtain the working tool positions relative to time. Thecorrelating of the structural model may include correlating each of theworking tool positions of the sensor data values with a closest elementof the structural model. The displaying of the structural model mayinclude setting a color of each correlated element of the structuralmodel in accordance with a determined color code to visually representthe respective sensor data value. The correlating of the structuralmodel with the sensor data values may include aligning the structuralmodel with a point cloud formed by the sensor data values relative tothe working tool positions. The display of the structural model mayinclude: assigning a color to each point of the point cloud inaccordance with a determined color code to visually represent therespective sensor data value; and displaying the point cloudsuperimposed on the structural model.

The process data may be arranged in layers, and the displaying of thestructural model in correspondence with the representation of the sensordata values at the working tool positions may be done on alayer-by-layer basis. The receiving of the sensor data may includesampling an output of the sensor to produce the sensor data. The toolmay include a laser and the sensor may include a pyrometer configured todetect a surface temperature of the part at a point where a beam of thelaser is impinging on the part.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the exemplary embodiments, and the manner inwhich the same are accomplished, will become more readily apparent withreference to the following detailed description taken in conjunctionwith the accompanying drawings.

FIG. 1 is a block diagram of an embodiment of a system for receivingsensor data for an additive manufacturing machine and mapping the sensordata with process data which controls the operation of the machine;

FIG. 2 is a block diagram of an embodiment of a system for receivingsensor data for a direct metal laser melting (DMLM) additivemanufacturing machine and mapping the sensor data with process data;

FIG. 3 is a block diagram of an embodiment of an edge gateway forreceiving sensor data for a DMLM machine;

FIG. 4 is a block diagram of an embodiment of a data host/server anduser interface device;

FIG. 5 is a flowchart of an embodiment of a process for mapping thesensor data with process data and rendering the result for display onthe user interface device;

FIG. 6 depicts an example of a user interface screen displaying sensordata for a layer of a part manufactured by a selected machine; and

FIG. 7 depicts a processing platform for implementing embodimentsdescribed herein.

DETAILED DESCRIPTION

FIG. 1 depicts a system 100 for receiving sensor data for an additivemanufacturing machine 110 (e.g., sensor output which is sampled anddigitized) and mapping the sensor data 120 with process data 130 whichcontrols the operation of the manufacturing machine 110. Themanufacturing machine 110 is computer controlled, e.g., by an internalprocessor having associated software which interacts with the variouscomponents of the machine 110. The software includes process data 130which provides instructions for the machine 110 to fabricate aparticular part. Such instructions may, for example, control theposition, movement, and power setting of a laser in an additivemanufacturing machine. The process data 130, in such a case, defines apath for the laser for each layer of a part. The process data 130 can bechanged, as necessary, to modify the manufacturing process to refine thephysical characteristics of a manufactured part or to produce adifferent part. In a typical case, the process data 130 is downloadedfrom a design facility 140, e.g., a computer-aided design and computeraided manufacturing (CAD/CAM) facility, and stored on the manufacturingmachine 110.

The manufacturing machine 110 has a number of sensors 120 which monitorvarious aspects of the manufacturing process. These may include sensors120 which measure the physical characteristics of a part beingmanufactured or worked on by the machine 110, such as, for example,temperature, pressure, acceleration, etc. It may also be desirable tohave sensors 120 which are separately installed in or near the machine110 to measure additional physical characteristics, e.g., a pyrometer tomeasure temperature during operation of an additive manufacturingmachine. Typically, the sensors 120 continuously output data during themanufacturing cycle, e.g., build time, of a part. For purposes ofdiscussion, the native sensors of the machine, as well as any separatelyinstalled sensors 120 which are monitoring the machine 110 (and/or thepart being manufactured by the machine), are considered to result in“sensor data for the machine.” In disclosed embodiments, the sensor data120 may come directly from the machine 110 and also may come fromseparately installed sensors 120 (in which case the sensor output issampled and digitized to produce the sensor data).

In disclosed embodiments, a context mapping 150 of the sensor data 120is performed which correlates the sensor data 120 with the process data130. In the resulting contextualized data, the various sensor 120readings are associated with the instructions in the process data 130based on a time correlation. Thus, for example, a sensor outputmeasuring acceleration of the laser of an additive manufacturing machinecan be aligned with specific directional and velocity instructions forthe laser in the process data. This allows engineering and design teamsto analyze data in the context of what was happening in themanufacturing process, rather than merely in the context of when thesensor measurement was taken. Therefore, for example, changes can bemade to the laser instructions to change the laser path to reduceacceleration below a desired threshold.

In disclosed embodiments, the system 100 may be used to provide modelsin association with a machine learning framework, for example, a“digital twin” 160 of a twinned physical system (e.g., the manufacturingmachine 110). A digital twin 160 may be a high fidelity, digital replicaor dynamic model of an asset (e.g., an additive manufacturing machine)or process, used to continuously gather data and increase insights,thereby helping to manage industrial assets at scale. A digital twin 160may leverage contextual data from the sensors 120 to represent nearreal-time status and operational conditions of an asset (e.g., themanufacturing machine 110) or process. A digital twin may estimate aremaining useful life of a twinned physical system using sensors,communications, modeling, history, and computation. It may provide ananswer in a time frame that is useful, i.e., meaningfully prior to aprojected occurrence of a failure event or suboptimal operation. Itmight comprise a code object with parameters and dimensions of itsphysical twin's parameters and dimensions that provide measured values,and keeps the values of those parameters and dimensions current byreceiving and updating values via contextual data from the sensorsassociated with the physical twin. The digital twin may comprise a realtime efficiency and life consumption state estimation device. It maycomprise a specific, or “per asset,” portfolio of system models andasset-specific sensors. It may receive inspection and/or operationaldata and track a single specific asset over its lifetime with observeddata and calculated state changes. Some digital twin models may includea functional or mathematical form that is the same for like assetsystems, but will have tracked parameters and state variables that arespecific to each individual asset system. The sophisticated insightsgained from analysis of contextual data and use of a digital twin 160can be passed on to design and engineering elements 140 to allow fordesign changes to improve the part or the manufacturing process (e.g.,the efficiency of the process).

FIG. 2 depicts an embodiment of a system 200 for receiving sensor datafor a direct metal laser melting (DMLM) additive manufacturing machine210 (including sensor output from separately installed sensors, which issampled and digitized) and mapping the sensor data with the processdata. The operation of the machine 210 as it fabricates a part 220 ismonitored by internal sensors of the machine, such as, for example,acceleration of the laser, humidity, and ambient temperature. The outputof these sensors may be communicated through the control and monitoringchannels 230 of the machine to a network computing and communicationdevice, such as, for example, an edge gateway server 240. Additionalsensors may be installed at the DMLM machine 210 to provide measurementdata for parameters not measured by the native sensors of the machine210. For example, a sensor tag 250 attached to and/or associated withthe part being manufactured may provide data wirelessly, e.g., a tagwhich measures the presence of particular gasses. As a further example,a pyrometer 260 may be installed at the manufacturing machine 210 tomeasure surface temperatures on a part 220 being manufactured, which mayserve as a measure of laser intensity. The output from these additionalsensors may be transmitted via separate channels (i.e., “out of band”)relative to the native sensors of the manufacturing machine, e.g., thesensor tag 250 data may be transmitted via a direct Bluetooth low energy(BLE) connection to the edge gateway and the pyrometer 260 data may betransmitted via a direct peripheral component interconnect bus dataacquisition (PCI DAQ).

As discussed in further detail below, the edge gateway 240 may receivesensor output data from one or more manufacturing machines 210 andperforms a context mapping (see, e.g., FIG. 1) with the process datawhich is used to control the one or more machines 210. The process datafor each machine 210 may be sent to the edge gateway via a control andmonitoring channel 230, i.e., the conventional data communicationchannel of the machine. Alternatively, the process data for a particularmachine may be transmitted from another source, e.g., a design andengineering facility, in advance of the operation of the machine andstored in a memory of the edge gateway. In addition to the contextmapping of the sensor output data, the edge gateway 240 filters and/orcompresses the sensor data before transmitting it, thereby reducing thelarge amount of sensor data into a data set that can be practicallytransmitted via a network 265 (e.g., the internet or another cloud-basednetwork).

The compressed, filtered data is received via the network 265 at a datahost/server 270. Both the edge gateway 240 and the data host/server 270may communicate using Predix™ which has been developed by GeneralElectric to provide cloud computing tools and techniques that provideapplication management and facilitate communication with industrialmachines in distributed locations. In such a case, the edge gateway 240and the data host/server 270 are equipped with an implementation ofPredix Edge, which enables these devices to communicate readily via thecloud-based network 265. The data host/server 270 performs various datastorage and management functions. It is connected to a user interfacedevice 275, such as, for example, a computer with a display and inputdevices. The user interface device 275 provides various ways ofdisplaying and analyzing the data from the data host/server 270.

FIG. 3 depicts an embodiment of an edge gateway 240 for receiving sensordata for a DMLM machine 210 (see FIG. 2), including sensor output fromseparately installed sensors, which may be sampled and digitized. Eachbox represents an app (i.e., software application) which performsparticular functions relating to the receiving and processing of sensordata. The boxes defined in this figure represent an embodiment of theedge gateway 240 in which the functions performed by the edge gateway240 are divided among a specific combination of apps. Other embodimentsof the edge gateway 240 may perform similar functions but may divide thefunctions among a different combination of apps (e.g., a combination inwhich the functions of two separate apps have been combined into asingle app, or vice versa). The apps run on a conventional computeroperating system, e.g., Microsoft Windows' or Linux.

The edge gateway 240 receives data from native sensors of themanufacturing machine 210 via the network 230 communication channelswhich are used for control and monitoring of the machine (see FIG. 2).Such communications may be handled by an implementation of Predix Edge305 in the edge gateway 240. In addition, there may be a data ingestionand sampling app 310 configured to receive out-of-band communicationsfrom additional sensors installed at the machine, such as, for example,a pyrometer. Prior to the data ingestion and sampling app 310, thereceiving of the out-of-band communications may be handled by areal-time operating system (RTOS) 308 which is configured to handle thedata as it is received in real time. In disclosed embodiments, an RTOSor a field programmable gate array (FPGA) is used to perform raw dataacquisition operations in deterministic time before the apps (e.g., thedata ingestion and sampling app 310) process the data. For example, somelaser systems produce a digital trigger signal that is captured in syncwith pyrometry data to provide microsecond accuracy the on/off times ofthe laser. The data ingestion and sampling app 310 may use specializedadapters to receive the out-of-band data and may provide signalconditioning, sampling at one or more specific rates (e.g., 50 kHz), andanalog to digital conversion. The specialized adapters may include anadapter configured to receive Bluetooth low energy (BLE) signals fromsensor tags. The edge gateway 240 also may include a central processingunit (CPU) management app 312 to control the use of multiple CPU coresto perform processing tasks more efficiently.

As noted above, the sensor data received from the pyrometer may beconsidered to be a measurement of laser intensity. To provide contextfor the intensity data, the edge gateway 240 maps the intensity datawith the position of the laser obtained from process data, taking intoaccount whether the laser is on or off at each position. To perform themapping, the model mark segmentation app 315 maps the intensity dataversus time. The process data relating to laser position and activationis stored in a common layer interface (CLI) file 320 in the form oflaser scanning and movement (i.e., “jump”) vectors arranged inlayers—the laser being on during a scanning vector and off during a jumpvector. The CLI file 320 is processed by the model mark segmentation app315 to produce the position of the laser versus time while the laser isturned on for each layer of the part. The model mark segmentation app315 correlates intensity and position of the laser to produce a data setfor intensity versus position (while the laser is on). The mapped, i.e.,correlated, data from the model mark segmentation app 315 is output to acompression app 325 to reduce data transmission requirements and is thensent to a store and forward app 330 where the data is stored in a bufferand forwarded for transmission via the network.

FIG. 4 depicts an embodiment of a data host/server 270 and userinterface device 275 for receiving and analyzing sensor data from theedge gateway 240, which is connected to the DMLM machine 210 (see FIGS.2 and 3). The sensor data may be, for example, laser intensity databased on pyrometer measurements. The data host/server 270 is connectedvia a network 265 to the edge gateway 240 (see FIG. 2) and may usePredix Edge 405 to facilitate communication and also to manage the appsrunning on the data host/server. The compressed laser intensity datatransmitted by the edge gateway 240 (see FIG. 2) is received at the datahost/server 270 by a compressed data ingestion app 410, where it isstored in a database 415. In disclosed embodiments, the ingested data isstored in correspondence with formation layers of the part beingmanufactured by the additive manufacturing machine. In such a case, theformation layers of the part may have an identifying number which allowsdata to be stored and retrieved based on a layer number. This allows auser to retrieve a set of laser intensity data for all (x, y) positionsin a particular layer in its compressed form. The data is made availableby the data host/server 270 via a web service 420 which is accessible byone or more user interface devices through a protocol such as, forexample, representational state transfer (REST).

The user interface device 275 provides a web app 425 which accesses theweb service 420 of the data host/server 270 to retrieve stored sensordata. The web app 425 may run as a standalone application on the nativeoperating system of the user interface device 275 or it may be accessedvia a browser running on the user interface device 275. Through the webapp 425, the user may request data from the web service 420, e.g., datafor a specific layer, in which case the relevant data is retrieved fromthe database 415 by the web service 420 and transmitted to the web app425 on the user interface device 275. In disclosed embodiments, the datafor the layer is decompressed by the user interface device 275, ratherthan by the data host/server 270. The decompression may be performed bythe rendering engine 430 (discussed below) or a separate app/modulerunning on the user interface device 275. This configuration allows thedata to be sent from the data host/server 270 to a number of userinterface devices 275 in compressed form, thereby reducing datacommunication requirements.

A rendering engine 430 is used to display the data in various formats,such as, for example, a three-dimensional perspective view. Therendering engine 430 may also color code the data so that the sensordata can be more easily visualized. For example, higher levels of laserintensity may be presented in red. The color coding may be applied bythe rendering engine 430 to elements of a structural model, e.g., a 3DCAD model, of the manufactured part. The 3D model of the part may bestored, e.g., in a 3D CAD stereolithography (STL) file. The userinterface device 275 may include Predix components 435 to facilitate thepresentation of a graphical user interface.

FIG. 5 depicts an embodiment of a process for mapping sensor data withprocess data and rendering the result for display on a user interfacedevice. A sensor output stream, e.g., from a pyrometer, is sampled at adetermined frequency (e.g., 50 kHz) and digitized 510. This results in astream of sensor data values at a known frequency. Therefore, eachsensor data value can be associated with a specific time relative to adetermined time reference 520. Manufacturing process data is retrievedto obtain information regarding the path of the tool (e.g., a laser)during manufacture of the part 530. The process data may be in the formof a CLI file, which specifies parameters relating to movement of thetool, such as the tool path (e.g., specified by position coordinatevectors and layer number), tool velocity, etc. The timing of theposition of the tool can be computed based on these movement parameters.The data in the CLI file also controls when the tool is on, i.e.,scanning or working, versus when it is merely moving from one positionto another. A vector which specifies movement while the tool is on maybe referred to as a “working vector”. The CLI file is segmented, i.e.,divided, to separate the working vectors from the non-working vectors oneach layer 540. This allows determination of the “working tool position”versus time for each layer, as both the position of the tool (while thetool is on) and the timing of its movement are known 550. The sensordata values versus time and the working tool positions versus time canbe time correlated to produce sensor data values versus working toolposition 560.

In disclosed embodiments, the time correlation is performed by aligningwaveforms of the sensor data values versus time with the working toolpositions versus time. Specifically, as the laser switches from an offcondition in a non-working vector to an on condition in a subsequentworking vector, and vice versa, these binary conditions produce a squarewave characteristic. The activation of the laser in the working vectorsresults in a significant increase in the sensor data values from thepyrometer, followed by a sharp drop in the sensor data values when thelaser is switched off in a subsequent non-working vector. Thus, thewaveform of the sensor data values is substantially a square wave,except that there is a delay relative to the working vector, and somerounding of the waveform, due to a natural lag between laseractivation/deactivation and the pyrometer readings. Nevertheless, thewaveforms of the sensor data values can be aligned with correspondingworking vectors to effectively achieve a time correlation therebetween.Alternatively, in disclosed embodiments, the time correlation may beperformed by stepping through the time data points of the working toolpositions versus time data, searching for a close time data point in thesensor data values versus time, and matching the corresponding sensordata value with the corresponding working tool position. Various otheralgorithms for correlating the data points with respect to time may beused.

The determination of sensor data values versus working tool positionprovides a space domain context in which to consider the sensor datavalues, i.e., the points in 3D space at which each sensor measurementwas made. The sensor data values in their space domain context form a“point cloud” of sensor data values at specific points in space which,in turn, can be correlated to the physical structure of the manufacturedpart, e.g., by using a 3D model of the part 570. For example, measuredlaser intensities can be correlated with the point in 3D space at whichthey are measured, and this point can be mapped to a corresponding pointor element on the surface of the manufactured part. In disclosedembodiments, the mapping is done on a layer-by-layer basis.

A 3D CAD model of the part may be used in the mapping of the measurementpoints of the sensor data values onto the physical structure of the partbecause process data files, e.g., CLI files, typically do not includeinformation regarding the physical structure of the part. For example,an STL file may be used as the source of the 3D model of the part. Thesensor data values in the point cloud can each be correlated, on alayer-by-layer basis, with the closest corresponding point/element ofthe 3D model (an STL file defines the physical structure as a shellrather than as a point cloud, so the correlation is not per sepoint-to-point). The corresponding elements of the 3D model can then bemodified to indicate the corresponding sensor data value, e.g., bysetting the element of the 3D model to a specific color based on adetermined color code. The 3D model with a representation of the sensordata (e.g., represented by a color code) can be rendered, according tolayer, on a display of the user interface device using conventionalrendering techniques 580. Alternatively, the point cloud of sensor datavalues could be color coded and separately rendered superimposed on the3D model on a layer-by-layer basis. In such a case, the point cloud ofsensor data values for a particular layer may be aligned as a whole withthe corresponding layer of the 3D model, rather than on a point-to-pointbasis, e.g., by alignment of designated reference points. The renderingand display of the 3D model and sensor data value point cloud is done ona layer-by-layer basis. In disclosed embodiments, the 3D model may bedisplayed as individual 2D models of each layer, e.g., by presenting aplan view of each layer.

FIG. 6 depicts an example of a user interface screen displaying sensordata for a part manufactured by a selected additive manufacturingmachine. A particular machine can be selected by a user, e.g., by makinga selection on a menu screen provided by the user interface web app. Indisclosed embodiments, the user also selects a specific layer to view(which may be identified by number). The user interface displays aninformation screen which includes an image of the selected layer of thepart, such as, for example, a perspective view of the top and the frontedge of the selected layer. The user interface also may display a laserpower level, laser beamsize specified for the layer in the process datafile, as well as the layer thickness. In addition, the user interfacemay display, in graphical form, parameters which are subject to changeover the course of scanning a layer, such as, for example, ambienttemperature, humidity, and laser acceleration.

The embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 7 illustrates aprocessing platform 700 that may be, for example, associated withcomponents of the systems disclosed herein. The processing platform 700comprises a processor 710, such as one or more commercially availablecentral processing units (CPUs) in the form of one-chip microprocessors,coupled to a communication device 720 configured to communicate via acommunication network (not shown). The communication device 720 may beused to communicate, for example, with one or more users. The processingplatform 700 further includes an input device 740 (e.g., a mouse and/orkeyboard to enter information) and an output device 750.

The processor 710 also communicates with a memory/storage device 730.The storage device 730 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 730 may store a program712 and/or processing logic 714 for controlling the processor 710. Theprocessor 710 performs instructions of the programs 712, 714, andthereby operates in accordance with any of the embodiments describedherein. For example, the processor 710 may receive data and then mayapply the instructions of the programs 712, 714 to carry out methodsdisclosed herein.

The programs 712, 714 may be stored in a compressed, uncompiled and/orencrypted format. The programs 712, 714 may furthermore include otherprogram elements, such as an operating system, a database managementsystem, and/or device drivers used by the processor 710 to interfacewith peripheral devices. Information may be received by or transmittedto, for example: (i) the platform 700 from another device; or (ii) asoftware application or module within the platform 700 from anothersoftware application, module, or any other source.

It is noted that aggregating data collected from or about multipleindustrial assets (e.g., manufacturing machines) may enable users toimprove operational performance. In an example, an industrial asset maybe outfitted with one or more sensors configured to monitor respectiveones of an asset's operations or conditions. Such sensors may alsomonitor the condition of a component part being processed, e.g.,manufacturec, by the asset. Data from the one or more sensors may berecorded or transmitted to a cloud-based or other remote computingenvironment. By bringing such data into a cloud-based computingenvironment, new software applications informed by industrial process,tools and know-how may be constructed, and new physics-based analyticsspecific to an industrial environment may be created. Insights gainedthrough analysis of such data may lead to enhanced asset designs, or toenhanced software algorithms for operating the same or similar asset atits edge, that is, at the extremes of its expected or availableoperating conditions. The data analysis also helps to develop betterservice and repair protocols and to improve manufacturing processes.

The systems and methods for managing industrial assets may include ormay be a portion of an Industrial Internet of Things (IIoT). In anexample, an IIoT connects industrial assets, such as turbines, jetengines, and locomotives, to the Internet or cloud, or to each other insome meaningful way. The systems and methods described herein mayinclude using a “cloud” or remote or distributed computing resource orservice. The cloud may be used to receive, relay, transmit, store,analyze, or otherwise process information for or about one or moreindustrial assets. In an example, a cloud computing system may includeat least one processor circuit, at least one database, and a pluralityof users or assets that may be in data communication with the cloudcomputing system. The cloud computing system may further include, or maybe coupled with, one or more other processor circuits or modulesconfigured to perform a specific task, such as to perform tasks relatedto asset maintenance, analytics, data storage, security, or some otherfunction. Such cloud-based systems may be used in conjunction with themessage queue-based systems described herein to provide widespread datacommunication with industrial assets, both new and old.

The flowcharts and/or block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts and/or block diagrams may represent a module, segment, orportion of code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowcharts, andcombinations of blocks in the block diagrams and/or flowcharts, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

Those in the art will appreciate that various adaptations andmodifications of the above-described embodiments can be configuredwithout departing from the scope and spirit of the claims. Therefore, itis to be understood that the claims may be practiced other than asspecifically described herein.

1. A method for receiving and processing sensor data for an operatingadditive manufacturing machine, the method comprising: receiving, at afirst server, the sensor data from a sensor for the operating additivemanufacturing machine; determining, using a processor of the firstserver, values of the sensor data relative to time; determining, usingthe processor of the first server, working tool positions relative totime based on process data which controls operation of the additivemanufacturing machine, the process data comprising vectors defining theworking tool positions during an additive manufacturing process;determining the sensor data values at the working tool positions basedon a correlation of the values of the sensor data relative to time andthe working tool positions relative to time; and displaying, on adisplay of the user interface device, a representation of the sensordata values at the working tool positions.
 2. The method of claim 1,further comprising correlating, using a processor of a user interfacedevice, a structural model of the part with the sensor data values atthe working tool positions, wherein the displaying comprises displayingat least a portion of the structural model in correspondence with therepresentation of the sensor data values at the working tool positions.3. The method of claim 2, wherein the correlating of the structuralmodel comprises correlating each of the working tool positions of thesensor data values with a closest element of the structural model. 4.The method of claim 2, wherein the displaying of the structural modelcomprises setting a color of each correlated element of the structuralmodel in accordance with a determined color code to visually representthe respective sensor data value.
 5. The method of claim 2, wherein thecorrelating of the structural model with the sensor data valuescomprises aligning the structural model with a point cloud formed by thesensor data values relative to the working tool positions.
 6. The methodof claim 5, wherein the display of the structural model comprisesassigning a color to each point of the point cloud in accordance with adetermined color code to visually represent the respective sensor datavalue; and displaying the point cloud superimposed on the structuralmodel.
 7. The method of claim 2, wherein the process data is arranged inlayers and the displaying of the structural model in correspondence withthe representation of the sensor data values at the working toolpositions is done on a layer-by-layer basis.
 8. The method of claim 1,wherein the receiving of the sensor data comprises sampling an output ofthe sensor to produce the sensor data.
 9. The method of claim 1, furthercomprising: segmenting, using the processor of the first server, theprocess data into the working vectors and non-working vectors; andfiltering, based on the working vectors, the tool positions relative totime to obtain the working tool positions relative to time.
 10. Themethod of claim 1, wherein the tool comprises a laser and the sensorcomprises a pyrometer configured to detect a surface temperature of thepart at a point where a beam of the laser is impinging on the part. 11.The method of claim 1, further comprising compressing the sensor dataand transmitting the sensor data via a network to a second server, thesecond server providing a web service to provide the sensor data to auser interface device.
 12. A system for receiving and processing sensordata for an operating additive manufacturing machine, the systemcomprising: a first server having a processor configured to perform:receiving the sensor data from a sensor for the operating additivemachine; determining values of the sensor data relative to time;determining working tool positions relative to time based on processdata which controls operation of the additive manufacturing machine, theprocess data comprising vectors defining the working tool positionsduring an additive manufacturing process; and determining the sensordata values at the working tool positions based on a correlation of thevalues of the sensor data relative to time and the working toolpositions relative to time; and a user interface device comprising adisplay and a processor configured to perform: displaying, on a displayof the user interface device, a representation of the sensor data valuesat the working tool positions.
 13. The system of claim 12, wherein theuser interface device is further configured to perform correlating,using the processor of the user interface device, a structural model ofthe part with the sensor data values at the working tool positions,wherein the displaying performed by the user interface device comprisesdisplaying at least a portion of the structural model in correspondencewith the representation of the sensor data values at the working toolpositions.
 14. The system of claim 13, wherein the correlating of thestructural model comprises correlating each of the working toolpositions of the sensor data values with a closest element of thestructural model.
 15. The system of claim 13, wherein the displaying ofthe structural model comprises setting a color of each correlatedelement of the structural model in accordance with a determined colorcode to visually represent the respective sensor data value.
 16. Thesystem of claim 13, wherein the correlating of the structural model withthe sensor data values comprises aligning the structural model with apoint cloud formed by the sensor data values relative to the workingtool positions.
 17. The system of claim 16, wherein the display of thestructural model comprises assigning a color to each point of the pointcloud in accordance with a determined color code to visually representthe respective sensor data value; and displaying the point cloudsuperimposed on the structural model.
 18. The system of claim 13,wherein the process data is arranged in layers and the displaying of thestructural model in correspondence with the representation of the sensordata values at the working tool positions is done on a layer-by-layerbasis.
 19. The system of claim 12, further comprising ananalog-to-digital converter configured to sample an output of the sensorto produce the sensor data.
 20. The system of claim 12, wherein theprocessor of the first server is further configured to perform:segmenting the process data into the working vectors and non-workingvectors; and filtering, based on the working vectors, the tool positionsrelative to time to obtain the working tool positions relative to time.21. The system of claim 12, wherein the tool comprises a laser and thesensor comprises a pyrometer configured to detect a surface temperatureof the part at a point where a beam of the laser is impinging on thepart.
 22. The system of claim 12, further comprising a second server,wherein the processor of the first server further performs compressingthe sensor data and transmitting the sensor data via a network to thesecond server, the second server providing a web service to provide thesensor data to the user interface device.
 23. A method for receiving andprocessing sensor data for an operating additive manufacturing machine,the method comprising: receiving, at a first server, the sensor datafrom a sensor for the operating additive manufacturing machine;determining, using a processor of the first server, values of the sensordata relative to time; determining, using the processor of the firstserver, working tool positions relative to time based on process datawhich controls operation of the additive manufacturing machine, theprocess data comprising vectors defining the working tool positionsduring an additive manufacturing process; determining the sensor datavalues at the working tool positions based on a correlation of thevalues of the sensor data relative to time and the working toolpositions relative to time; outputting the sensor data values at theworking tool positions to an analytic model of the additivemanufacturing machine; receiving, from the analytic model of theadditive manufacturing machine, adjusted process data, the adjustedprocess data being determined based at least in part on the analyticmodel and the sensor data values at the working tool positions, theanalytic model being based at least in part on a measured characteristicof the additive manufacturing machine; and using the adjusted processdata to control the operation of the additive manufacturing machine.